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AWS Machine Learning Engineer

ML Engineer I

Who We Are:

Born digital, UST transforms lives through the power of technology. We walk alongside our clients and partners, embedding innovation and agility into everything they do. We help them create transformative experiences and human-centered solutions for a better world.

UST is a mission-driven group of 29,000+ practical problem solvers and creative thinkers in more than 30 countries. Our entrepreneurial teams are empowered to innovate, act nimbly, and create a lasting and sustainable impact for our clients, their customers, and the communities in which we live.

With us, you'll create a boundless impact that transforms your career-and the lives of people across the world.

Visit us at UST.com.

You Are:

UST is seeking an AWS Machine Learning Engineer to build, deploy, and optimize production-grade machine learning solutions on AWS.

This role is ideal for a hands-on engineer who can work across the ML lifecycle from data preparation and feature engineering through model training, evaluation, deployment, and monitoring using AWS-native services and modern MLOps practices.

The opportunity:

* Design, develop, and productionize ML solutions on AWS using services such as Amazon SageMaker, Amazon S3, AWS Lambda, AWS Step Functions, and related analytics/integration services.

* Build and maintain reproducible training and inference workflows, including data preprocessing, model training, evaluation, and deployment automation.

* Implement real-time, batch, serverless, or multi-model inference patterns based on business and performance needs.

* Develop APIs or service integrations for model consumption by downstream applications.

* Collaborate with data scientists, platform engineers, DevOps teams, and application teams to operationalize models reliably and securely.

* Monitor model performance, data quality, drift, and operational health in production; support retraining and continuous improvement processes.

* Apply AWS security and governance best practices including IAM, encryption, logging, and auditable deployments.

This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.

What you need:

* Technical Skills:

* 5+ years in software/ML engineering, with strong hands-on experience in Python.

* 2+ years of production experience with Amazon SageMaker for training and/or inference deployments.

* Strong grasp of supervised/unsupervised ML pipelines, model evaluation, feature engineering, and experiment reproducibility.

* Experience with SageMaker Pipelines, model packaging/registration concepts, and deployment automation.

* Strong knowledge of AWS core services: S3, IAM, Lambda, CloudWatch, API Gateway, ECR, and networking fundamentals.

* Experience enforcing least-privilege access, data isolation, token-based authentication (OAuth2/JWT)

* Proficient in the Model Context Protocol (MCP) open standard, with hands on experience setting up custom MCP Servers using official TypeScript or Python SDKs.

* Hands On experience integrating custom MCP servers into Agentic AI frameworks

* Solid understanding of security vectors unique to LLM orchestration, including tool validation, API sandboxing, input sanitization, and enterprise access control.

* Experience building RESTful or event-driven services to expose ML capabilities.

* Experience with Amazon Bedrock or generative AI integration patterns on AWS.

* Experience with MLflow, experiment tracking, or model registry tooling.

* Experience with Feature Store, data quality checks, or model bias/fairness validation.

* Familiarity with Docker/containerized ML workloads.

* Understanding of model monitoring, operational metrics, logging, and troubleshooting in production.

* Knowledge of cloud security basics including least privilege, encryption, and secure secret/configuration handling.

* Nice-to-Have Skills:

* Polyglot Engineering Capabilities (Multi language fluency)

* Familiarity with Terraform or CloudFormation for infrastructure as code.

* Exposure to streaming/event-driven data pipelines using AWS-native services.

Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.

Role Location: Illinois

Compensation Range: $72,000-$108,000

Benefits

Full-time, regular employees accrue a minimum of 10 days of paid vacation per year, receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year), 10 paid holidays, and are eligible for paid bereavement leave and jury duty. They are eligible to participate in the Company's 401(k) Retirement Plan with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance, as well as the following Company-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short- and long-term disability benefits. Regular employees may purchase additional voluntary short-term disability benefits, and participate in a Health Savings Account (HSA) as well as a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines. Benefits offerings vary in Puerto Rico.

Part-time employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year) and are eligible to participate in the Company's 401(k) Retirement Plan with employer matching.

Full-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year) and are eligible to participate in the Company's 401(k) program with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance.

Part-time temporary employees receive 6 days of paid sick leave each year (pro-rated for new hires throughout the year).

All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws.

What we believe:

We proudly embrace the values that have shaped UST since day one. We build our culture of Humility, Humanity, and Integrity. These values inspire us to nurture a people-first, human centric culture that fosters diversity, prioritizes sustainable solutions, and keeps our people and clients at the forefront of all decisions.

Humility:

We will listen, learn, be empathetic and help selflessly in our interactions with everyone.

Humanity:

Through business, we will better the lives of those less fortunate than ourselves.

Integrity:

We honor our commitments and act with responsibility in all our relationships.

Equal Employment Opportunity Statement

UST is an Equal Opportunity Employer.

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. We will consider qualified applicants with arrest or conviction records in accordance with state and local laws and "fair chance" ordinances.

UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.

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amazon sagemaker,machine learning,aws,python,amazon s3,aws lambda,aws step functions,